SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1221–1230 of 3304 papers

TitleStatusHype
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Uniform Manifold Approximation with Two-phase OptimizationCode1
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Novel optimized crow search algorithm for feature selectionCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified